TY - GEN
T1 - Towards Semantic Detection of Smells in Cloud Infrastructure Code.
AU - Kumara, Indika
AU - Vasileiou, Zoe
AU - Meditskos, Georgios
AU - Tamburri, Damian A.
AU - Heuvel, Willem-Jan van den
AU - Karakostas, Anastasios
AU - Vrochidis, Stefanos
AU - Kompatsiaris, Ioannis
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2020
Y1 - 2020
N2 - Automated deployment and management of Cloud applications relies on descriptions of their deployment topologies, often referred to as Infrastructure Code. As the complexity of applications and their deployment models increases, developers inadvertently introduce software smells to such code specifications, for instance, violations of good coding practices, modular structure, and more. This paper presents a knowledge-driven approach enabling developers to identify the aforementioned smells in deployment descriptions. We detect smells with SPARQL-based rules over pattern-based OWL 2 knowledge graphs capturing deployment models. We show the feasibility of our approach with a prototype and three case studies.
AB - Automated deployment and management of Cloud applications relies on descriptions of their deployment topologies, often referred to as Infrastructure Code. As the complexity of applications and their deployment models increases, developers inadvertently introduce software smells to such code specifications, for instance, violations of good coding practices, modular structure, and more. This paper presents a knowledge-driven approach enabling developers to identify the aforementioned smells in deployment descriptions. We detect smells with SPARQL-based rules over pattern-based OWL 2 knowledge graphs capturing deployment models. We show the feasibility of our approach with a prototype and three case studies.
KW - Cloud Computing
KW - Defects
KW - Deployment
KW - Infrastructure Code
KW - Infrastructure Code Smells
KW - OWL 2
KW - TOSCA
UR - http://www.scopus.com/inward/record.url?scp=85091505460&partnerID=8YFLogxK
U2 - 10.1145/3405962.3405979
DO - 10.1145/3405962.3405979
M3 - Conference contribution
T3 - PervasiveHealth: Pervasive Computing Technologies for Healthcare
SP - 63
EP - 67
BT - WIMS 2020: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics
ER -